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The extra cost of comorbidity: multiple illnesses and the economic burden of non-communicable diseases

Overview of attention for article published in BMC Medicine, December 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
policy
1 policy source
twitter
9 X users

Citations

dimensions_citation
79 Dimensions

Readers on

mendeley
133 Mendeley
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Title
The extra cost of comorbidity: multiple illnesses and the economic burden of non-communicable diseases
Published in
BMC Medicine, December 2017
DOI 10.1186/s12916-017-0978-2
Pubmed ID
Authors

Sébastien Cortaredona, Bruno Ventelou

Abstract

The literature offers competing estimates of disease costs, with each study having its own data and methods. In 2007, the Dutch Center for Public Health Forecasting of the National Institute for Public Health and the Environment provided guidelines that can be used to set up cost-of-illness (COI) studies, emphasising that most COI analyses have trouble accounting for comorbidity in their cost estimations. When a patient has more than one chronic condition, the conditions may interact such that the patient's healthcare costs are greater than the sum of the costs for the individual diseases. The main objective of this work was to estimate the costs of 10 non-communicable diseases when their co-occurrence is acknowledged and properly assessed. The French Echantillon Généraliste de Bénéficiaires (EGB) database was used to assign all healthcare expenses for a representative sample of the population covered by the National Health Insurance. COIs were estimated in a bottom-up approach, through regressions on individuals' healthcare expenditure. Two-way interactions between the 10 chronic disease variables were included in the expenditure model to account for possible effect modification in the presence of comorbidity(ies). The costs of the 10 selected chronic diseases were substantially higher for individuals with comorbidity, demonstrating the pattern of super-additive costs in cases of diseases interaction. For instance, the cost associated with diabetes for people without comorbidity was estimated at 1776 €, whereas this was 2634 € for people with heart disease as a comorbidity. Overall, we detected 41 cases of super-additivity over 45 possible comorbidities. When simulating a preventive action on diabetes, our results showed that significant monetary savings could be achieved not only for diabetes itself, but also for the chronic diseases frequently associated with diabetes. When comorbidity exists and where super-additivity is involved, a given preventive policy leads to greater monetary savings than the costs associated with the single diagnosis, meaning that the returns from the action are generally underestimated.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 133 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 17%
Student > Master 22 17%
Student > Ph. D. Student 14 11%
Student > Bachelor 9 7%
Professor 5 4%
Other 20 15%
Unknown 41 31%
Readers by discipline Count As %
Medicine and Dentistry 22 17%
Nursing and Health Professions 17 13%
Economics, Econometrics and Finance 9 7%
Pharmacology, Toxicology and Pharmaceutical Science 5 4%
Agricultural and Biological Sciences 4 3%
Other 25 19%
Unknown 51 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 December 2023.
All research outputs
#1,833,982
of 25,059,640 outputs
Outputs from BMC Medicine
#1,305
of 3,921 outputs
Outputs of similar age
#40,672
of 451,771 outputs
Outputs of similar age from BMC Medicine
#17
of 47 outputs
Altmetric has tracked 25,059,640 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,921 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 45.6. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 451,771 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.